After the fair training of the model we are able to interpret the classification and investigate in the influence of the sensitive attribute on the classification task [1]}. We do so by reattaching the sensitive attribute \(z\)  to the fair model again, see Fig. . For better interpretability the fair feature representation is is linearly combined, forming \(r\) . The reattachment of \(z\)  and its interpretation is possible as \(r\)  is trained to be independent of \(z\)  (see also [2]})
\(\hat{y} = f(w_r r + w_z z + b), \text{with } z \perp \!\!\! \perp r\) 
